Overview

Dataset statistics

Number of variables10
Number of observations2768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory216.4 KiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Outcome has constant value "1"Constant
Pregnancies is highly overall correlated with AgeHigh correlation
SkinThickness is highly overall correlated with BMIHigh correlation
BMI is highly overall correlated with SkinThicknessHigh correlation
Age is highly overall correlated with PregnanciesHigh correlation
Id is uniformly distributedUniform
Id has unique valuesUnique

Reproduction

Analysis started2023-09-19 17:25:44.473002
Analysis finished2023-09-19 17:26:08.561941
Duration24.09 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct2768
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1384.5
Minimum1
Maximum2768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:08.728819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile139.35
Q1692.75
median1384.5
Q32076.25
95-th percentile2629.65
Maximum2768
Range2767
Interquartile range (IQR)1383.5

Descriptive statistics

Standard deviation799.1971
Coefficient of variation (CV)0.57724601
Kurtosis-1.2
Mean1384.5
Median Absolute Deviation (MAD)692
Skewness0
Sum3832296
Variance638716
MonotonicityStrictly increasing
2023-09-19T17:26:08.999015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1850 1
 
< 0.1%
1842 1
 
< 0.1%
1843 1
 
< 0.1%
1844 1
 
< 0.1%
1845 1
 
< 0.1%
1846 1
 
< 0.1%
1847 1
 
< 0.1%
1848 1
 
< 0.1%
1849 1
 
< 0.1%
Other values (2758) 2758
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2768 1
< 0.1%
2767 1
< 0.1%
2766 1
< 0.1%
2765 1
< 0.1%
2764 1
< 0.1%
2763 1
< 0.1%
2762 1
< 0.1%
2761 1
< 0.1%
2760 1
< 0.1%
2759 1
< 0.1%

Pregnancies
Real number (ℝ)

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3972835
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:09.253016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum17
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9320791
Coefficient of variation (CV)0.66679329
Kurtosis0.89036837
Mean4.3972835
Median Absolute Deviation (MAD)2
Skewness1.027433
Sum12171.681
Variance8.597088
MonotonicityNot monotonic
2023-09-19T17:26:09.490641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 491
17.7%
4.397283531 412
14.9%
2 387
14.0%
3 270
9.8%
4 259
9.4%
5 198
7.2%
6 181
 
6.5%
7 145
 
5.2%
8 134
 
4.8%
9 98
 
3.5%
Other values (7) 193
 
7.0%
ValueCountFrequency (%)
1 491
17.7%
2 387
14.0%
3 270
9.8%
4 259
9.4%
4.397283531 412
14.9%
5 198
7.2%
6 181
 
6.5%
7 145
 
5.2%
8 134
 
4.8%
9 98
 
3.5%
ValueCountFrequency (%)
17 4
 
0.1%
15 3
 
0.1%
14 9
 
0.3%
13 32
 
1.2%
12 32
 
1.2%
11 35
 
1.3%
10 78
2.8%
9 98
3.5%
8 134
4.8%
7 145
5.2%

Glucose
Real number (ℝ)

Distinct136
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.89527
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:09.772508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199
median118
Q3141
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.50096
Coefficient of variation (CV)0.25022267
Kurtosis-0.32133555
Mean121.89527
Median Absolute Deviation (MAD)20
Skewness0.51815371
Sum337406.11
Variance930.30858
MonotonicityNot monotonic
2023-09-19T17:26:10.068412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 66
 
2.4%
100 61
 
2.2%
102 52
 
1.9%
129 51
 
1.8%
106 50
 
1.8%
95 49
 
1.8%
112 49
 
1.8%
105 47
 
1.7%
111 47
 
1.7%
108 46
 
1.7%
Other values (126) 2250
81.3%
ValueCountFrequency (%)
44 3
 
0.1%
56 4
 
0.1%
57 7
0.3%
61 4
 
0.1%
62 3
 
0.1%
65 4
 
0.1%
67 3
 
0.1%
68 10
0.4%
71 13
0.5%
72 5
 
0.2%
ValueCountFrequency (%)
199 4
 
0.1%
198 3
 
0.1%
197 12
0.4%
196 8
0.3%
195 10
0.4%
194 13
0.5%
193 8
0.3%
191 3
 
0.1%
190 4
 
0.1%
189 12
0.4%

BloodPressure
Real number (ℝ)

Distinct47
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.404086
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:10.366609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.988255
Coefficient of variation (CV)0.16557429
Kurtosis1.0848212
Mean72.404086
Median Absolute Deviation (MAD)8
Skewness0.19284984
Sum200414.51
Variance143.71826
MonotonicityNot monotonic
2023-09-19T17:26:10.661831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 201
 
7.3%
74 197
 
7.1%
78 173
 
6.2%
68 170
 
6.1%
64 163
 
5.9%
72 162
 
5.9%
80 138
 
5.0%
76 132
 
4.8%
60 129
 
4.7%
62 128
 
4.6%
Other values (37) 1175
42.4%
ValueCountFrequency (%)
24 3
 
0.1%
30 5
 
0.2%
38 4
 
0.1%
40 3
 
0.1%
44 15
 
0.5%
46 8
 
0.3%
48 18
0.7%
50 44
1.6%
52 40
1.4%
54 42
1.5%
ValueCountFrequency (%)
122 4
 
0.1%
114 4
 
0.1%
110 10
0.4%
108 7
0.3%
106 12
0.4%
104 7
0.3%
102 4
 
0.1%
100 12
0.4%
98 11
0.4%
96 12
0.4%

SkinThickness
Real number (ℝ)

Distinct53
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.289634
Minimum7
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:10.937549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q125
median29.289634
Q332
95-th percentile44
Maximum110
Range103
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.0312654
Coefficient of variation (CV)0.3083434
Kurtosis8.373126
Mean29.289634
Median Absolute Deviation (MAD)3.7103659
Skewness1.1637619
Sum81073.707
Variance81.563754
MonotonicityNot monotonic
2023-09-19T17:26:11.211883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.28963415 800
28.9%
32 114
 
4.1%
30 102
 
3.7%
23 82
 
3.0%
27 81
 
2.9%
28 74
 
2.7%
18 74
 
2.7%
33 71
 
2.6%
39 70
 
2.5%
31 69
 
2.5%
Other values (43) 1231
44.5%
ValueCountFrequency (%)
7 5
 
0.2%
8 8
 
0.3%
10 18
 
0.7%
11 20
 
0.7%
12 28
1.0%
13 41
1.5%
14 21
 
0.8%
15 47
1.7%
16 21
 
0.8%
17 55
2.0%
ValueCountFrequency (%)
110 2
 
0.1%
99 3
 
0.1%
63 4
 
0.1%
60 3
 
0.1%
59 2
 
0.1%
56 4
 
0.1%
54 6
0.2%
52 6
0.2%
51 4
 
0.1%
50 10
0.4%

Insulin
Real number (ℝ)

Distinct187
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.23783
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:11.495515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile50
Q1120
median154.23783
Q3154.23783
95-th percentile293
Maximum846
Range832
Interquartile range (IQR)34.23783

Descriptive statistics

Standard deviation81.678056
Coefficient of variation (CV)0.52955916
Kurtosis12.596531
Mean154.23783
Median Absolute Deviation (MAD)5.7621697
Skewness2.7431577
Sum426930.31
Variance6671.3049
MonotonicityNot monotonic
2023-09-19T17:26:11.779148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154.2378303 1330
48.0%
105 42
 
1.5%
140 33
 
1.2%
130 31
 
1.1%
180 30
 
1.1%
120 29
 
1.0%
100 27
 
1.0%
94 24
 
0.9%
135 23
 
0.8%
76 22
 
0.8%
Other values (177) 1177
42.5%
ValueCountFrequency (%)
14 4
 
0.1%
15 4
 
0.1%
16 4
 
0.1%
18 7
0.3%
22 4
 
0.1%
23 6
0.2%
25 3
 
0.1%
29 4
 
0.1%
32 3
 
0.1%
36 11
0.4%
ValueCountFrequency (%)
846 1
 
< 0.1%
744 3
0.1%
680 3
0.1%
600 3
0.1%
579 5
0.2%
545 3
0.1%
543 1
 
< 0.1%
540 4
0.1%
510 4
0.1%
495 7
0.3%

BMI
Real number (ℝ)

Distinct253
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.596665
Minimum18.2
Maximum80.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:12.060527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.2
Q127.575
median32.4
Q336.625
95-th percentile44.6
Maximum80.6
Range62.4
Interquartile range (IQR)9.05

Descriptive statistics

Standard deviation7.1034241
Coefficient of variation (CV)0.21791873
Kurtosis2.4063121
Mean32.596665
Median Absolute Deviation (MAD)4.6
Skewness0.85229216
Sum90227.57
Variance50.458634
MonotonicityNot monotonic
2023-09-19T17:26:12.352695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 46
 
1.7%
31.2 45
 
1.6%
31.6 41
 
1.5%
32.59666545 39
 
1.4%
33.3 37
 
1.3%
32.4 35
 
1.3%
32.8 34
 
1.2%
30.8 33
 
1.2%
32.9 33
 
1.2%
30.1 31
 
1.1%
Other values (243) 2394
86.5%
ValueCountFrequency (%)
18.2 11
0.4%
18.4 3
 
0.1%
19.1 3
 
0.1%
19.3 4
 
0.1%
19.4 3
 
0.1%
19.5 8
0.3%
19.6 9
0.3%
19.9 1
 
< 0.1%
20 4
 
0.1%
20.1 6
0.2%
ValueCountFrequency (%)
80.6 2
 
0.1%
67.1 4
0.1%
64.4 2
 
0.1%
59.4 4
0.1%
57.3 4
0.1%
55 4
0.1%
53.2 4
0.1%
52.9 4
0.1%
52.7 2
 
0.1%
52.3 6
0.2%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct523
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47119256
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:12.663625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.141
Q10.244
median0.375
Q30.624
95-th percentile1.136
Maximum2.42
Range2.342
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.32566883
Coefficient of variation (CV)0.69115869
Kurtosis5.172935
Mean0.47119256
Median Absolute Deviation (MAD)0.168
Skewness1.8427907
Sum1304.261
Variance0.10606019
MonotonicityNot monotonic
2023-09-19T17:26:12.968366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258 22
 
0.8%
0.207 20
 
0.7%
0.261 18
 
0.7%
0.268 18
 
0.7%
0.238 18
 
0.7%
0.259 17
 
0.6%
0.284 16
 
0.6%
0.551 16
 
0.6%
0.52 16
 
0.6%
0.292 16
 
0.6%
Other values (513) 2591
93.6%
ValueCountFrequency (%)
0.078 3
 
0.1%
0.084 3
 
0.1%
0.085 7
0.3%
0.088 8
0.3%
0.089 3
 
0.1%
0.092 3
 
0.1%
0.096 4
0.1%
0.1 4
0.1%
0.101 3
 
0.1%
0.102 3
 
0.1%
ValueCountFrequency (%)
2.42 4
0.1%
2.329 3
0.1%
2.288 1
 
< 0.1%
2.137 4
0.1%
1.893 3
0.1%
1.781 3
0.1%
1.731 4
0.1%
1.699 3
0.1%
1.698 4
0.1%
1.6 4
0.1%

Age
Real number (ℝ)

Distinct52
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.132225
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2023-09-19T17:26:13.259782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q340
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.77723
Coefficient of variation (CV)0.35546148
Kurtosis0.77185874
Mean33.132225
Median Absolute Deviation (MAD)7
Skewness1.1662989
Sum91710
Variance138.70315
MonotonicityNot monotonic
2023-09-19T17:26:13.551568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 264
 
9.5%
21 229
 
8.3%
25 182
 
6.6%
24 168
 
6.1%
23 141
 
5.1%
28 133
 
4.8%
26 117
 
4.2%
27 113
 
4.1%
29 99
 
3.6%
31 82
 
3.0%
Other values (42) 1240
44.8%
ValueCountFrequency (%)
21 229
8.3%
22 264
9.5%
23 141
5.1%
24 168
6.1%
25 182
6.6%
26 117
4.2%
27 113
4.1%
28 133
4.8%
29 99
 
3.6%
30 77
 
2.8%
ValueCountFrequency (%)
81 4
 
0.1%
72 4
 
0.1%
70 4
 
0.1%
69 8
0.3%
68 4
 
0.1%
67 13
0.5%
66 16
0.6%
65 11
0.4%
64 4
 
0.1%
63 17
0.6%

Outcome
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
1
2768 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2768
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2768
100.0%

Length

2023-09-19T17:26:13.818802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T17:26:14.027010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 2768
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2768
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2768
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2768
100.0%

Interactions

2023-09-19T17:26:05.355630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:44.783976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:47.205280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:50.168658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:52.865060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:55.343315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:57.472021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:59.645534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:02.061976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:05.603384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:45.025764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:47.452173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:50.510659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:53.121357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:55.567230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:57.715685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:59.876103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:02.438151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:05.856124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:45.295840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:47.734799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:50.933298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:53.377811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:55.827719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:57.956202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:00.146248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:02.835340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:06.102457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:45.553873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:48.083484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:51.323566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:53.640949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:56.069932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:58.218281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:00.426731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:03.236731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:06.347238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:45.790484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:48.436962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:51.638232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:53.867236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:56.313738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:58.462281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:00.665289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:03.622440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:06.593736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:46.235126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:48.806994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:51.871400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:54.114819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:56.525490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:58.686452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:00.886750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:03.992744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:07.135091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:46.458764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:49.099215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:52.121137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:54.343393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:56.743990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:58.911062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:01.113280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:04.352847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:07.384823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:46.696589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:49.441679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:52.378234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:54.599650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:56.972484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:59.155097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:01.377081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:04.730672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:07.641855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:46.946758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:49.817367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:52.625655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:54.842231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:57.226909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:25:59.412816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:01.676403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T17:26:05.077090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-19T17:26:14.189631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
IdPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAge
Id1.000-0.0300.0110.0090.0110.0120.019-0.003-0.008
Pregnancies-0.0301.0000.1710.2660.1790.1200.110-0.0260.550
Glucose0.0110.1711.0000.2300.1980.3990.2210.0770.272
BloodPressure0.0090.2660.2301.0000.2110.1090.2810.0060.362
SkinThickness0.0110.1790.1980.2111.0000.1920.5450.0440.191
Insulin0.0120.1200.3990.1090.1921.0000.1830.0460.183
BMI0.0190.1100.2210.2810.5450.1831.0000.1330.110
DiabetesPedigreeFunction-0.003-0.0260.0770.0060.0440.0460.1331.0000.039
Age-0.0080.5500.2720.3620.1910.1830.1100.0391.000

Missing values

2023-09-19T17:26:07.972993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-19T17:26:08.373799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
016.000000148.072.00000035.000000154.2378333.6000000.627501
121.00000085.066.00000029.000000154.2378326.6000000.351311
238.000000183.064.00000029.289634154.2378323.3000000.672321
341.00000089.066.00000023.00000094.0000028.1000000.167211
454.397284137.040.00000035.000000168.0000043.1000002.288331
565.000000116.074.00000029.289634154.2378325.6000000.201301
673.00000078.050.00000032.00000088.0000031.0000000.248261
7810.000000115.072.40408629.289634154.2378335.3000000.134291
892.000000197.070.00000045.000000543.0000030.5000000.158531
9108.000000125.096.00000029.289634154.2378332.5966650.232541
IdPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
275827593.000000111.090.00000012.00000078.0000028.40.495291
275927606.000000102.082.00000029.289634154.2378330.80.180361
276027616.000000134.070.00000023.000000130.0000035.40.542291
276127622.00000087.072.40408623.000000154.2378328.90.773251
276227631.00000079.060.00000042.00000048.0000043.50.678231
276327642.00000075.064.00000024.00000055.0000029.70.370331
276427658.000000179.072.00000042.000000130.0000032.70.719361
276527666.00000085.078.00000029.289634154.2378331.20.382421
276627674.397284129.0110.00000046.000000130.0000067.10.319261
276727682.00000081.072.00000015.00000076.0000030.10.547251